The growing philosophical literature on algorithmic fairness has examined statistical criteria such as equalized odds and calibration, causal and counterfactual approaches, and the role of structural and compounding injustices. Yet an important dimension has been overlooked: whether the evidential value of an algorithmic output itself depends on structural injustice. We contrast a predictive policing algorithm, which relies on historical crime data, with a camera-based system that records ongoing offenses, where both are designed to guide police deployment. In evaluating the moral acceptability of acting on a piece of evidence, we must ask not only whether the evidence is probative in the actual world, but also whether it would remain probative in nearby worlds without the relevant injustices. The predictive policing algorithm fails this test, but the camera-based system passes it. When evidence fails the test, it is morally problematic to use it punitively, more so than evidence that passes the test.

In this short article, I leverage the National Crime Victimization Survey from 1992 to 2022 to examine how income, education, employment, and key demographic factors shape the type of crime victims experience (violent vs property). Using balanced classification splits and logistic regression models evaluated by F1-score, there is an isolation of the socioeconomic drivers of victimization "Group A" models and then an introduction of demographic factors such as age, gender, race, and marital status controls called "Group B" models. The results consistently proves that higher income and education lower the odds of violent relative to property crime, while men younger individuals and racial minorities face disproportionately higher violentcrime risks. On the geographic spectrum, the suburban models achieve the strongest predictive performance with an accuracy of 0.607 and F1 of 0.590, urban areas benefit from adding education and employment predictors and crime in rural areas are still unpredictable using these current factors. The patterns found in this study shows the need for specific interventions like educational investments in metropolitan settings economic support in rural communities and demographicaware prevention strategies.

Domestic violence, which is often perceived as a gendered issue among female victims, has gained increasing attention in recent years. Despite this focus, male victims of domestic abuse remain primarily overlooked, particularly in Bangladesh. Our study represents a pioneering exploration of the underexplored realm of male domestic violence (MDV) within the Bangladeshi context, shedding light on its prevalence, patterns, and underlying factors. Existing literature predominantly emphasizes female victimization in domestic violence scenarios, leading to an absence of research on male victims. We collected data from the major cities of Bangladesh and conducted exploratory data analysis to understand the underlying dynamics. We implemented 11 traditional machine learning models with default and optimized hyperparameters, 2 deep learning, and 4 ensemble models. Despite various approaches, CatBoost has emerged as the top performer due to its native support for categorical features, efficient handling of missing values, and robust regularization techniques, achieving 76% accuracy. In contrast, other models achieved accuracy rates in the range of 58-75%. The eXplainable AI techniques, SHAP and LIME, were employed to gain insights into the decision-making of black-box machine learning models. By shedding light on this topic and identifying factors associated with domestic abuse, the study contributes to identifying groups of people vulnerable to MDV, raising awareness, and informing policies and interventions aimed at reducing MDV. Our findings challenge the prevailing notion that domestic abuse primarily affects women, thus emphasizing the need for tailored interventions and support systems for male victims. ML techniques enhance the analysis and understanding of the data, providing valuable insights for developing effective strategies to combat this pressing social issue.

Last week, A.I.–generated nude images of pop superstar Taylor Swift were produced and distributed without her consent. They circulated throughout the internet, with one single post on X (née Twitter) garnering 45 million views before the site took it down. Deepfakes, as they've come to be called in recent years, often target female celebrities, but with the rise of A.I., it's easier than ever for everyday people (almost always women) to be targeted. Last year, more than 143,000 deepfake porn videos were created, according to one estimate from the independent researcher Genevieve Oh, more than every other previous year combined. That number will, in all likelihood, only continue to rise.

Deepfake technology tools have become ubiquitous, "democratizing" the ability to manipulate images and videos. One popular use of such technology is the creation of sexually explicit content, which can then be posted and shared widely on the internet. This article examines attitudes and behaviors related to non-consensual synthetic intimate imagery (NSII) across over 16,000 respondents in 10 countries. Despite nascent societal awareness of NSII, NSII behaviors were considered harmful. In regards to prevalence, 2.2% of all respondents indicated personal victimization, and 1.8% all of respondents indicated perpetration behaviors. Respondents from countries with relevant legislation also reported perpetration and victimization experiences, suggesting legislative action alone is not a sufficient solution to deter perpetration. Technical considerations to reduce harms may include suggestions for how individuals can better monitor their presence online, as well as enforced platform policies which ban, or allow for removal of, NSII content.

Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are commonly known as hot spot predictive policing. These systems predict where future crime is likely to concentrate such that police can allocate patrols to these areas and deter crime before it occurs. Previous research on fairness in predictive policing has concentrated on the feedback loops which occur when models are trained on discovered crime data, but has limited implications for models trained on victim crime reporting data. We demonstrate how differential victim crime reporting rates across geographical areas can lead to outcome disparities in common crime hot spot prediction models. Our analysis is based on a simulation patterned after district-level victimization and crime reporting survey data for Bogot\'a, Colombia. Our results suggest that differential crime reporting rates can lead to a displacement of predicted hotspots from high crime but low reporting areas to high or medium crime and high reporting areas. This may lead to misallocations both in the form of over-policing and under-policing.

Researchers, including two of Indian-origin, have developed an artificial intelligence (AI) algorithm which can help predict susceptibility to substance use disorder among young homeless individuals. The study, presented at the International Joint Conference on Artificial Intelligence, revealed that this algorithm can suggest personalized rehabilitation programs for highly susceptible homeless youth. "Proactive prevention of substance use disorder among homeless youth is much more desirable than reactive mitigation strategies such as medical treatments for the disorder and other related interventions," said study author Amulya Yadav from the Penn State University in the US. For the results, the research team built the model using a dataset collected from approximately 1,400 homeless youth, ages 18 to 26, in six US states. The dataset was collected by the Research, Education, and Advocacy Co-Lab for Youth Stability and Thriving (REALIST), which includes Anamika Barman-Adhikari, assistant professor at the University of Denver and co-author of the paper.

In this paper, we proposed a novel automated model, called Vulnerability Index for Population at Risk (VIPAR) scores, to identify rare populations for their future shooting victimizations. Likewise, the focused deterrence approach identifies vulnerable individuals and offers certain types of treatments (e.g., outreach services) to prevent violence in communities. The proposed rule-based engine model is the first AI-based model for victim prediction. This paper aims to compare the list of focused deterrence strategy with the VIPAR score list regarding their predictive power for the future shooting victimizations. Drawing on the criminological studies, the model uses age, past criminal history, and peer influence as the main predictors of future violence. Social network analysis is employed to measure the influence of peers on the outcome variable. The model also uses logistic regression analysis to verify the variable selections. Our empirical results show that VIPAR scores predict 25.8% of future shooting victims and 32.2% of future shooting suspects, whereas focused deterrence list predicts 13% of future shooting victims and 9.4% of future shooting suspects. The model outperforms the intelligence list of focused deterrence policies in predicting the future fatal and non-fatal shootings. Furthermore, we discuss the concerns about the presumption of innocence right.